No abstract

No abstract

In this paper, dynamic behavior analysis has been discussed for a class of switched complex-valued neural networks with interval parameter uncertainties and impulse disturbance. Sufficient conditions for guaranteeing the existence, uniqueness, and global robust exponential stability of the equilibrium point have been obtained by using the homomorphism mapping theorem, the scalar Lyapunov function method, the average dwell time method, and M-matrix theory. Since there is no result concerning the stability problem of switched neural networks defined in complex number domain, the stability results we describe in this paper generalize the existing ones. The effectiveness of the proposed results is illustrated by a numerical example.

In this paper, a class of large-scale systems with impulsive effect, input disturbance, and both variable and unbounded delays were investigated. On the assumption that all subsystems of the large-scale system can be exponentially stabilized, and the stabilizing feedbacks and corresponding Lyapunov functions (LFs) for the closed-loop systems are available, using the idea of vector Lyapunov method and M-matrix property, the intero-differential inequalities with variable and unbounded delays were constructed. By the stability analysis of the intero-differential inequalities, the sufficient conditions to ensure the robust exponential stability of the large-scale system were obtained. Finally, the correctness and validity of the methods was verified by two numerical examples.

No abstract